tomaarsen/splade-distilbert-base-uncased-nq

Distilbert base trained on Natural-Questions tuples

This is a Sparse Encoder model finetuned from distilbert/distilbert-base-uncased on the natural-questions dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

• Model Type: Sparse Encoder
• Base model: distilbert/distilbert-base-uncased
• Maximum Sequence Length: 512 tokens
• Training Dataset:
  • natural-questions
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Sparse Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sparse Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-nq")
# Run inference
sentences = [
    'is send in the clowns from a musical',
    'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
    'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Sparse Information Retrieval

• Datasets: NanoMSMARCO, NanoNFCorpus and NanoNQ
• Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ
dot_accuracy@1	0.32	0.32	0.48
dot_accuracy@3	0.48	0.42	0.68
dot_accuracy@5	0.56	0.48	0.72
dot_accuracy@10	0.72	0.58	0.76
dot_precision@1	0.32	0.32	0.48
dot_precision@3	0.16	0.2867	0.2267
dot_precision@5	0.112	0.284	0.144
dot_precision@10	0.072	0.236	0.08
dot_recall@1	0.32	0.0191	0.46
dot_recall@3	0.48	0.0491	0.64
dot_recall@5	0.56	0.0667	0.67
dot_recall@10	0.72	0.0893	0.73
dot_ndcg@10	0.498	0.27	0.6094
dot_mrr@10	0.4297	0.3939	0.5884
dot_map@100	0.441	0.1121	0.5709

Sparse Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with SparseNanoBEIREvaluator with these parameters:

  {
      "dataset_names": [
          "msmarco",
          "nfcorpus",
          "nq"
      ]
  }
  

Metric	Value
dot_accuracy@1	0.3733
dot_accuracy@3	0.5267
dot_accuracy@5	0.5867
dot_accuracy@10	0.6867
dot_precision@1	0.3733
dot_precision@3	0.2244
dot_precision@5	0.18
dot_precision@10	0.1293
dot_recall@1	0.2664
dot_recall@3	0.3897
dot_recall@5	0.4322
dot_recall@10	0.5131
dot_ndcg@10	0.4591
dot_mrr@10	0.4707
dot_map@100	0.3747

Training Details

Training Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 99,000 training samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 29 characters mean: 46.96 characters max: 93 characters	min: 10 characters mean: 582.13 characters max: 2141 characters

• Samples:

  query	answer
  who played the father in papa don't preach	Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.
  where was the location of the battle of hastings	Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.
  how many puppies can a dog give birth to	Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]

• Loss: SpladeLoss with these parameters:

  {'loss': SparseMultipleNegativesRankingLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
    )
    (cross_entropy_loss): CrossEntropyLoss()
  ), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
    )
  ), 'query_regularizer': FlopsLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
    )
  )}
  

Evaluation Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 1,000 evaluation samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 30 characters mean: 47.2 characters max: 96 characters	min: 58 characters mean: 598.96 characters max: 2480 characters

• Samples:

  query	answer
  where is the tiber river located in italy	Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.
  what kind of car does jay gatsby drive	Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.
  who sings if i can dream about you	I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]

• Loss: SpladeLoss with these parameters:

  {'loss': SparseMultipleNegativesRankingLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
    )
    (cross_entropy_loss): CrossEntropyLoss()
  ), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
    )
  ), 'query_regularizer': FlopsLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
    )
  )}
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• tp_size: 0
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_dot_ndcg@10	NanoNFCorpus_dot_ndcg@10	NanoNQ_dot_ndcg@10	NanoBEIR_mean_dot_ndcg@10
0.0162	100	153.0226	-	-	-	-	-
0.0323	200	13.1878	-	-	-	-	-
0.0485	300	2.103	-	-	-	-	-
0.0646	400	0.3687	-	-	-	-	-
0.0808	500	0.2077	-	-	-	-	-
0.0970	600	0.0595	-	-	-	-	-
0.1131	700	0.0715	-	-	-	-	-
0.1293	800	0.112	-	-	-	-	-
0.1454	900	0.0362	-	-	-	-	-
0.1616	1000	0.0442	0.0489	0.4540	0.2450	0.4984	0.3991
0.1778	1100	0.0321	-	-	-	-	-
0.1939	1200	0.0307	-	-	-	-	-
0.2101	1300	0.0443	-	-	-	-	-
0.2262	1400	0.0381	-	-	-	-	-
0.2424	1500	0.0302	-	-	-	-	-
0.2586	1600	0.0267	-	-	-	-	-
0.2747	1700	0.0541	-	-	-	-	-
0.2909	1800	0.0472	-	-	-	-	-
0.3070	1900	0.027	-	-	-	-	-
0.3232	2000	0.0615	0.0393	0.4904	0.2834	0.5711	0.4483
0.3394	2100	0.0183	-	-	-	-	-
0.3555	2200	0.0312	-	-	-	-	-
0.3717	2300	0.0196	-	-	-	-	-
0.3878	2400	0.0451	-	-	-	-	-
0.4040	2500	0.036	-	-	-	-	-
0.4202	2600	0.0487	-	-	-	-	-
0.4363	2700	0.0195	-	-	-	-	-
0.4525	2800	0.0452	-	-	-	-	-
0.4686	2900	0.0227	-	-	-	-	-
0.4848	3000	0.0154	0.0369	0.4398	0.2864	0.6094	0.4452
0.5010	3100	0.0183	-	-	-	-	-
0.5171	3200	0.0072	-	-	-	-	-
0.5333	3300	0.0262	-	-	-	-	-
0.5495	3400	0.0202	-	-	-	-	-
0.5656	3500	0.0392	-	-	-	-	-
0.5818	3600	0.015	-	-	-	-	-
0.5979	3700	0.0146	-	-	-	-	-
0.6141	3800	0.0172	-	-	-	-	-
0.6303	3900	0.0361	-	-	-	-	-
0.6464	4000	0.0163	0.0293	0.4673	0.2867	0.6111	0.4551
0.6626	4100	0.019	-	-	-	-	-
0.6787	4200	0.0196	-	-	-	-	-
0.6949	4300	0.0286	-	-	-	-	-
0.7111	4400	0.0391	-	-	-	-	-
0.7272	4500	0.0253	-	-	-	-	-
0.7434	4600	0.0155	-	-	-	-	-
0.7595	4700	0.0367	-	-	-	-	-
0.7757	4800	0.0213	-	-	-	-	-
0.7919	4900	0.0155	-	-	-	-	-
0.8080	5000	0.0242	0.0279	0.4984	0.2568	0.5949	0.4500
0.8242	5100	0.011	-	-	-	-	-
0.8403	5200	0.0145	-	-	-	-	-
0.8565	5300	0.0086	-	-	-	-	-
0.8727	5400	0.0254	-	-	-	-	-
0.8888	5500	0.0137	-	-	-	-	-
0.9050	5600	0.0117	-	-	-	-	-
0.9211	5700	0.0171	-	-	-	-	-
0.9373	5800	0.0192	-	-	-	-	-
0.9535	5900	0.0203	-	-	-	-	-
0.9696	6000	0.0239	0.0245	0.4987	0.2674	0.6058	0.4573
0.9858	6100	0.0233	-	-	-	-	-
-1	-1	-	-	0.4980	0.2700	0.6094	0.4591

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Energy Consumed: 0.100 kWh
• Carbon Emitted: 0.039 kg of CO2
• Hours Used: 0.285 hours

Training Hardware

• On Cloud: No
• GPU Model: 1 x NVIDIA GeForce RTX 3090
• CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
• RAM Size: 31.78 GB

Framework Versions

• Python: 3.11.6
• Sentence Transformers: 4.2.0.dev0
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.1
• Datasets: 2.21.0
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

SpladeLoss

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}